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- W4247901641 abstract "The sizes of hidden data sources are of great interests to public, researchers and even business competitors. Estimating the size of hidden data sources has been a challenging problem. Most existing methods are derived from the classic capture-recapture methods. Another approach is based on a large query pool. This method is not accurate due to the large variance of the document frequencies of queries in the query pool. Targeting this problem, we propose a new method to reduce the variance by constructing a query pool from a sample of the target data source so that document frequency variance is reduced, yet most of the documents can be covered. Our method is tested on a variety of large textual corpora, and outperforms the baseline random query method and the Broder et al's estimation method on all the datasets." @default.
- W4247901641 created "2022-05-12" @default.
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- W4247901641 date "2014-08-01" @default.
- W4247901641 modified "2023-10-14" @default.
- W4247901641 title "Estimating the size of hidden data sources by queries" @default.
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- W4247901641 doi "https://doi.org/10.1109/asonam.2014.6921664" @default.
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